RSeQC is a comprehensive quality control (QC) tool for RNA-seq experiments. It evaluates various aspects of RNA-seq data, including sequence quality, GC bias, PCR bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity, and read distribution over the genome. RSeQC takes SAM and BAM files as input, which are produced by most RNA-seq mapping tools, and also BED files, which are widely used for gene models. Most modules use R scripts for visualization and are efficient in handling large BAM/SAM files with hundreds of millions of alignments. RSeQC is written in Python and C, and its source code and user manual are freely available online. It includes basic modules for raw sequence quality evaluation, RNA-seq-specific modules for annotation-based checking, and utility modules for data visualization. RSeQC is more comprehensive and efficient than other QC tools and has unique checks not available elsewhere. It includes modules for checking mapping statistics, inner distance distribution between paired reads, gene body coverage, read distribution, RPKM saturation, junction saturation, experimental design inference, junction annotation, RPKM count, and BAM to wiggle file conversion. RSeQC helps ensure the quality of RNA-seq data by assessing sequencing saturation, coverage uniformity, and other important metrics, which are crucial for accurate RNA-seq analysis. Funding was provided by the Department of Defense Prostate Cancer Program and the Cancer Prevention and Research Institute of Texas. No conflicts of interest were declared.RSeQC is a comprehensive quality control (QC) tool for RNA-seq experiments. It evaluates various aspects of RNA-seq data, including sequence quality, GC bias, PCR bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity, and read distribution over the genome. RSeQC takes SAM and BAM files as input, which are produced by most RNA-seq mapping tools, and also BED files, which are widely used for gene models. Most modules use R scripts for visualization and are efficient in handling large BAM/SAM files with hundreds of millions of alignments. RSeQC is written in Python and C, and its source code and user manual are freely available online. It includes basic modules for raw sequence quality evaluation, RNA-seq-specific modules for annotation-based checking, and utility modules for data visualization. RSeQC is more comprehensive and efficient than other QC tools and has unique checks not available elsewhere. It includes modules for checking mapping statistics, inner distance distribution between paired reads, gene body coverage, read distribution, RPKM saturation, junction saturation, experimental design inference, junction annotation, RPKM count, and BAM to wiggle file conversion. RSeQC helps ensure the quality of RNA-seq data by assessing sequencing saturation, coverage uniformity, and other important metrics, which are crucial for accurate RNA-seq analysis. Funding was provided by the Department of Defense Prostate Cancer Program and the Cancer Prevention and Research Institute of Texas. No conflicts of interest were declared.